Power Converters and AC Electrical Drives with Linear Neural Networks

A recent trend for the development of these disciplines is the application of artificial intelligence (AI) tools, such as expert systems (ES), artificial neural networks (ANN), fuzzy logic systems (FLS), genetic algorithms (GA), and, more recently, multi-agent systems (MAS). These tools have been proven to be able to boost the performance of these systems in real-world and industrial applications thanks to features such as "learning," "self-organization," and "self-adaptation." With particular regard to ANNs for nonlinear function approximation, in power electronics and electrical drives applications, they are used for control and identification, such as the multilayer perceptron (MLP) or the radial basis function (RBF). Another kind of neuron that has also been applied recently is linear neurons (ADALINE), whose simplicity has given surprisingly good results. On the other hand, the detailed unified mathematical treatment of space-vectors has made it possible to embed the theory of linear neural networks, resulting in improvements, both theoretical and experimental, of classical approaches in electrical drives and power electronics. This standpoint is the goal of the book: to present in a systematic way the classical theory based on space-vectors in identification, control of electrical drives and of power converters, and the improvements that can be attained when using linear neural networks. With this outlook, this book is divided into four parts: o Part I deals specifically with voltage source inverters (VSI) and their control. o Part II deals with AC electrical drive control, with particular attention to induction and permanent magnet synchronous motor drives. o Part III deals with theoretical aspects of linear neural networks. o Part IV deals with specific applications of linear neural networks to electrical drives and power quality. Chapter 1 presents the theory of space-vectors and instantaneous power. This chapter is fundamental for understanding the rest of the book. Chapter 2 describes the open-loop and closed-loop control of voltage source inverters. With regard to open-loop techniques it also explains the different kinds of pulsewidth modulation (PWM) strategies, and with regard to closed-loop techniques it analyzes both current and power control of VSIs. Voltage-oriented control (VOC) and direct power control (DPC) are also presented. Chapter 3 explains the fundamentals of power quality; parallel active filters (PAFs) and series active filters (SAFs), with reference to their operating principle and control strategies, are investigated. Passive and hybrid filter configurations are also analyzed. Chapter 4 deals with induction machine (IM) static and dynamic space-vector models. The dynamic model of the IM, including saturation effects, is shown. Finally, the spacevector dynamic model of the IM, including rotor and stator slotting effects, is described. Chapter 5 describes, first, scalar control strategies of IM drives with impressed voltages and currents. It then derives field-oriented control (FOC) strategies, with reference to rotor, stator, and magnetizing flux linkage orientations. Related flux models are also presented. Finally, direct torque control (DTC) strategies are presented, particularly the classic switching table (ST) DTC, the space-vector modulation (SVM) DTC, and the electromagnetically compatible (DTC). The so-called direct self-control (DSC) is also described. Chapter 6 covers sensorless control of IM drives, with particular reference to both model-based and anisotropy-based techniques. With regard to model-based techniques, the following estimators/observers are described: open-loop speed estimators, model reference adaptive systems (MRAS), full-order Luenberger adaptive observer (FOLO), full-order s